JP6645150B2 - Information providing apparatus, information providing method, and program - Google Patents

Description

The present invention relates to an information providing technique, and more particularly to an information providing apparatus, an information providing method , and a program for providing information on an item used by a user.

  2. Description of the Related Art Conventionally, there is a technique of classifying users (customers) who use a store or a product for each attribute, and totaling the number of sales of the product for each user attribute to create marketing information. For example, at a store (conveyor belt sushi restaurant), the product (sushi material) ordered by a customer is recorded in a database together with customer demographic information such as gender, age group, and constituent members of the customer, and the data is aggregated. Another product order ranking is created (for example, see Patent Document 1). In addition, there is a technology that detects position information of a plurality of mobile terminal devices, groups mobile terminals having common position information over a predetermined time interval, and provides optimal information to the group (for example, Patent Document 1). 2).

JP 2010-231390 A JP 2004-320217 A

  When a user visits a store, the user may act alone or in a group such as a family, a friend, or a person at work. According to the technology described in Patent Literature 1, customers can be classified by using a classification such as a single customer, a gender pair, a family, a male group, and a female group as constituent members. In the technique described in Patent Document 2, the type of group (couple, parent and child, family, etc.) is estimated by multiplying personal information. On the other hand, when the user acts in a group and selects a store or a product, the influence of each member on the decision making is not always equal, but rather different in many cases. However, so far, no consideration has been given to the difference in the influence of members on the selection of stores and products.

  The present invention has been made in view of such a situation, and an object of the present invention is to create information on the influence of members in a group regarding the selection of a store or a product while reducing the burden on customers and store operators. It is to provide technology.

  In order to solve the above-described problems, an information providing apparatus according to an aspect of the present invention obtains position information of a subject that is a user or a terminal device, and based on time-series data of position information related to a plurality of subjects, The influence of the subjects in the group on each of the subjects included in the group formed by the group forming unit based on the time series data of the location information based on the group forming unit forming the group including the subject. Is calculated by a point calculation unit that calculates a point for each subject based on the characteristic activity amount, and a point calculation unit that calculates a point indicating a degree of matching with a predetermined behavior pattern that is considered to be high. The tally processing unit calculates the item points for each item by tallying the points, and outputs the item points calculated by the tally processing unit And an output section that, a.

  Another embodiment of the present invention also relates to an information providing device. This apparatus obtains position information of a subject which is a user or a terminal device, and, based on time-series data of position information relating to a plurality of subjects, forms a group including a plurality of subjects based on a time series data; Based on the time series data, the degree of matching of a predetermined behavior pattern in which the influence of the subject in the group is considered to be high for each of the plurality of subjects included in the group formed by the group forming unit is indicated. A point calculation unit that calculates a characteristic action amount and calculates a point for each subject based on the characteristic action amount; and an output unit that outputs the point calculated by the point calculation unit in association with information related to the item. , Is provided.

Still another embodiment of the present invention relates to an information providing method. This method is an information providing method executed by a computer, which obtains position information of a subject which is a user or a terminal device, and includes a plurality of subjects based on time-series data of position information relating to the plurality of subjects. Forming a group, and, based on the time-series data of the position information, a predetermined behavior pattern in which the influence of the subject in the group is considered to be high for each of the plurality of subjects included in the formed group. Calculating a characteristic action amount indicating the degree of matching, and calculating a point for each subject based on the characteristic action amount; and calculating an item point for each item by totalizing the calculated points. If, comprising the steps of outputting the item points calculated, the.

Yet another embodiment of the present invention also relates to an information providing method. This method is an information providing method executed by a computer, which obtains position information of a subject which is a user or a terminal device, and includes a plurality of subjects based on time-series data of position information relating to the plurality of subjects. Forming a group, and, based on the time-series data of the position information, a predetermined behavior pattern in which the influence of the subject in the group is considered to be high for each of the plurality of subjects included in the formed group. calculating a characteristic behavior amount indicating a degree of matching based on the feature amount of activity, for each entity, calculating a point, a step of outputting associating the calculated points according to the item information, the Including .

  Note that any combination of the above-described components and any conversion of the expression of the present invention between a method, an apparatus, a system, a recording medium, a computer program, and the like are also effective as embodiments of the present invention.

  ADVANTAGE OF THE INVENTION According to this invention, the information regarding the influence of the member in a group regarding the selection of a store or an article can be created, reducing the burden on a customer and the store operator.

FIG. 1 is a diagram illustrating a configuration of an information providing system according to a first embodiment of the present invention. FIG. 2 is a diagram illustrating a configuration of the information providing apparatus of FIG. 1. FIG. 3 is a diagram illustrating a data structure of a database stored in a position information management unit in FIG. 2. FIG. 3 is a diagram illustrating a data structure of a database stored in a store information storage unit in FIG. 2. 3 is a flowchart showing a procedure for determining a leader point by the information providing apparatus of FIG. 2. FIG. 3 is a diagram illustrating a data structure of a database stored in a group forming unit in FIG. 2. FIG. 3 is a diagram illustrating a data structure of a database stored in a group information processing unit in FIG. 2. 3 is a flowchart illustrating a procedure for updating a leader point by the information providing apparatus of FIG. 2. FIG. 3 is a diagram illustrating an outline of a process in a point calculation unit in FIG. 2. FIGS. 10A and 10B are diagrams illustrating an outline of another process in the point calculation unit in FIG. FIG. 3 is a diagram showing a data structure of a point history table of FIG. 2. 3 is a flowchart illustrating a procedure for counting leader points by the information providing apparatus of FIG. 2. FIG. 3 is a diagram illustrating a data structure of a database stored in a tally processing unit in FIG. 2. FIG. 2 is a diagram illustrating information displayed on a management client in FIG. 1. FIG. 4 is a diagram illustrating another information displayed on the management client in FIG. 1. FIG. 2 is a layout diagram of stores in a shopping mall to be processed by the information providing system of FIG. 1. FIG. 6 is a diagram showing still another information displayed on the management client in FIG. 1. FIGS. 18A and 18B are diagrams showing further information displayed on the management client in FIG. FIGS. 19A and 19B are diagrams showing further information displayed on the management client in FIG. FIG. 6 is a diagram illustrating a configuration of an information providing apparatus according to a second embodiment of the present invention. FIG. 16 is a diagram illustrating a data structure of a database stored in a user information storage unit in FIG. 15. FIG. 16 is a diagram illustrating a data structure of another database stored in the user information storage unit of FIG. 15. FIG. 14 is a diagram illustrating information displayed on the management client according to the second embodiment of the present invention. FIG. 14 is a diagram illustrating another information displayed on the management client according to the second embodiment of the present invention. FIG. 14 is a diagram illustrating still another information displayed on the management client according to the second embodiment of the present invention. FIG. 14 is a diagram illustrating still another information displayed on the management client according to the second embodiment of the present invention. FIG. 13 is a diagram illustrating a data structure of another database stored in the user information storage unit according to the third embodiment of the present invention. FIG. 13 is a diagram illustrating a data structure of a user-specific point totalization table according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating a data structure of another user-specific point totaling table according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating a data structure of a purchase history table according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating information displayed on a management client according to a fourth embodiment of the present invention. FIG. 14 is a diagram illustrating another information displayed on the management client according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating further information displayed on the management client according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating further information displayed on the management client according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating further information displayed on the management client according to the fourth embodiment of the present invention. FIG. 14 is a diagram illustrating a data structure of a database stored in a group information processing unit according to a fifth embodiment of the present invention. FIG. 14 is a diagram illustrating a data structure of a point history table according to the fifth embodiment of the present invention. 15 is a flowchart illustrating a procedure for correcting a leader point by the information providing apparatus according to the fifth embodiment of the present invention.

(Example 1)
Before describing the present invention in detail, an overview will first be given. The first embodiment of the present invention relates to an information providing system including an information providing device, a plurality of terminal devices connectable to the information providing device, and a management client connectable to the information providing device. A group is formed by the users having each of the plurality of terminal devices acting together. When making a decision in a group, for example, when deciding a restaurant to enter for a meal, the preference of a user having a large influence in the group may be reflected. Therefore, the information providing apparatus according to the present embodiment forms a group using the position information of a plurality of terminal devices, estimates the influence of each user in the group, and derives the influence of the user as a leader point. I do.

  Further, the information providing device generates information in which leader points are totaled in association with stores, products, and the like, that is, leader points (item points) for stores, products, and the like. At that time, the information providing apparatus classifies the leader points into a plurality of categories (leader point categories) based on a predetermined rule, and generates display data indicating the frequency distribution of the leader point categories for each store, product, and the like. Thereby, a plurality of users (customers) who came to a store such as a restaurant are classified into a predetermined type based on the user's action history, and the number of users for each type is calculated, thereby generating customer analysis information. Is done. The customer analysis information is provided to a store operator or an operator of a store aggregate such as a shopping mall or a shopping mall.

  Next, information provided in the present embodiment will be described. The information providing device provides information on the item to the group. The “item” is a term indicating the general object used by the user, and is, for example, a store, a product, a service, a Web page, digital content, or the like. In the present embodiment, the item is a store such as a restaurant. The above is the summary description.

  FIG. 1 shows a configuration of an information providing system 100 according to the first embodiment of the present invention. The information providing system 100 includes a first terminal device 16a, a second terminal device 16b, a third terminal device 16c, an N-th terminal device 16n, collectively referred to as an information providing device 10, a network 12, a base station device 14, and a terminal device 16. The management client 18 is included.

  The terminal device 16 corresponds to a wireless communication system and connects to a base station device 14 described later. An example of the wireless communication system is a mobile phone system, a wireless LAN (Local Area Network) system, a business wireless system, or the like, but is not limited thereto. The terminal device 16 is a mobile phone, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a wireless device, or the like, and is configured to be easily carried by a user. The terminal device 16 has a function of acquiring position information (latitude, longitude, and the like) using a GPS (Global Positioning System), a wireless LAN, or the like. In addition, the terminal device 16 may include an acceleration sensor and a gyro sensor in addition to the GPS data acquisition function, and may calculate more accurate position information using the acceleration sensor and the gyro sensor.

  On one end side, the base station apparatus 14 supports the same wireless communication system as the terminal apparatus 16 and can connect to the terminal apparatus 16. The base station device 14 connects the network 12 at the other end. The network 12 is connected to a base station device 14. The network 12 may be anything, for example, a wired network, a wireless network, or a combination thereof.

  The information providing device 10 is connected to a network 12. With such a configuration, the terminal device 16 can communicate with the information providing device 10 via the base station device 14 and the network 12. Therefore, the terminal device 16 transmits the acquired position information to the information providing device 10 via the base station device 14 and the network 12. The information providing apparatus 10 generates information based on the received position information, and transmits the information to a later-described management client 18 via the network 12 and the base station apparatus 14. The processing in the information providing device 10 will be described later.

  The management client 18 is a device used by an operator such as a store. The management client 18 includes an input unit such as a keyboard and a mouse, and a display unit such as a display, and can use a general PC. Further, a plurality of management clients 18 may exist. The management client 18 is connected to the information providing device 10 via the network 12. The network 12 for connecting the information providing device 10 and the management client 18 may be the same as or different from the network 12 for connecting the information providing device 10 and the terminal device 16. .

  FIG. 2 shows a configuration of the information providing apparatus 10. The information providing device 10 includes a transmission / reception unit 20, a position information management unit 22, a group information processing unit 24, a tally processing unit 26, and a store information storage unit 28, and the group information processing unit 24 includes a group formation unit 40, a point calculation unit 42, and the tallying processing unit 26 includes a point history table 44. The information providing apparatus 10 can be implemented as a computer program to be executed using a general computer including a CPU, a RAM, a ROM, an HDD, a network interface, and the like. Further, a distributed processing system may be constructed in which each unit of the information providing apparatus 10 is assigned to a plurality of computers. Further, the information providing device 10 and the management client 18 can be implemented by one device (one computer).

  The position information management unit 22 stores a position information table in which position information periodically transmitted from each terminal device 16, a terminal ID, and date and time information are associated with each other. FIG. 3 shows an example, and shows a data structure of a database stored in the position information management unit 22. The position information includes at least the information of the latitude and the longitude, but other information such as the altitude, the moving direction, and the acceleration may be received from the terminal device 16 and stored. In the present embodiment, it is assumed that one user occupies and uses one terminal device 16 like a mobile phone / smartphone, and the terminal ID and the user ID are substantially equal. . In this embodiment, for each terminal ID, the position information of the terminal and a leader point to be described later are managed, but a user ID for identifying the user may be used. It is of course possible to cope with a mode in which one terminal device 16 is used by a plurality of users by using the user ID. Further, the term “subject identifier” may be used to mean an identifier that uniquely identifies a subject that is a terminal or a user.

  It is estimated that the group forming unit 40 is acting (moving) together using the time-series position information stored in the position information management unit 22, that is, the time-series data of the position information of a plurality of use subjects. The terminal devices 16 are specified and grouped. The point calculating unit 42 estimates the influence of the members in the group on the decision making using the time-series position information. In the present embodiment, the selection of a restaurant or the like is taken as an example of the decision, but other decision, such as selection of a product, may be handled. A numerical value called a leader point (influence value) is calculated by the group information processing unit as an index indicating the influence of each member. The method of calculating the leader point will be described later in detail.

  FIG. 4 shows a data structure of a database stored in the store information storage unit 28. In the present embodiment, a restaurant will be described as an example of a store, but of course, the present invention is not limited to this. The store information storage unit stores the store ID for identifying the store, the genre of the meal served by the store, the position information of the store (latitude and longitude), and the category (leader point large), “Rp medium”, Store information table that is at least associated with “Rp small”) is stored. In addition, information on the number of visitors to the store, the price range of products handled at the store, information on the average price of one payment, information on the average payment price per person, and the like may be stored. Further, one store may correspond to a plurality of cooking genres. For example, a store may support both “Chinese food” and “Japanese food”.

  The aggregation processing unit 26 aggregates the number of customers for each leader point type of each store based on the information in the group information processing unit 24 and the store information storage unit 28, and provides the total number of visitors to operating organizations such as stores and shopping malls. The aggregation processing unit 26 will be described later in detail. The control unit 30 is a processing unit that controls the entire information providing apparatus 10, and has a timekeeping function such as a real-time clock. The transmission / reception unit 20 transmits / receives data between the management client 18 and the terminal device 16 via the network. For example, the transmission / reception unit 20 periodically receives the position information, the terminal ID, and the date and time information from each terminal device 16 via the base station device 14 and the network 12. The transmission / reception unit 20 transmits information such as a leader point to the management client 18 via the transmission / reception unit 20.

  Next, the operation of the group information processing unit 24 will be described using the flowchart of FIG. FIG. 5 is a flowchart showing a procedure for determining a leader point by the information providing apparatus 10. In step S100, the group forming unit 40 forms a group including a plurality of subjects based on the time-series data of the location information on the plurality of subjects stored in the location information management unit 22. A group is equivalent to grouping entities that are presumed to be acting (moving) together. That is, the group forming unit 40 determines which terminal device 16 belongs to the group, and forms a group. More specifically, the group forming unit 40 uses the data stored in the position information management unit 22 to determine whether a plurality of terminal devices 16 exist within a predetermined distance for a predetermined time or more. , The terminal devices 16 are determined to be one group. The predetermined time is set, for example, to 1 to 10 minutes. The predetermined distance is set, for example, to 1 to 50 m.

  Here, when the date and time recorded in the position information management unit 22 is slightly different for each terminal ID, it is desirable to correct the latitude and longitude according to the difference between the date and time between the terminal IDs. For example, the date and time of the first terminal device 16a whose terminal ID is “A” is “2015/03/20 10:11:25” and the same as the second terminal device 16b whose terminal ID is “B” When there is no position information of the date and time, two pieces of position information of the date and time adjacent to “2015/03/2010 10:11:25” are extracted from the position information of the second terminal device 16b. When the two pieces of position information are, for example, “2015/03/20 10:11:00” and “2015/03/20 10:11:26”, the two pieces of position information are interpolated into the second terminal. The latitude and longitude of “2015/03/20 10:11:25” of the device 16b are calculated. In this example, a position that internally divides the position at “2015/03/20 10:11:00” and the position at “2015/03/20 10:11:26” into 25: 1 is calculated. Note that the interpolation of the position information is not limited to such linear interpolation, and a non-linear interpolation process using three or more pieces of position information may be performed.

  Here, one method of performing group determination in the group forming unit 40 will be described with reference to FIG. FIG. 6 shows a data structure of a database stored in the group forming unit 40. This is because, in a certain time zone (for example, 2015/1/1 10:00 to 10:03), the first terminal device 16a to the eighth terminal device 16h existing in an area of a predetermined range and the respective terminal devices Shows the average distance (m). The terminal ID of the first terminal device 16a is indicated as “A”, the terminal ID of the second terminal device 16b is indicated as “B”, the terminal ID of the third terminal device 16c is indicated as “C”, The terminal ID of the fourth terminal device 16d is indicated as "D". Similarly, the terminal ID of the eighth terminal device 16h is indicated as "H". The average distance is calculated by calculating the distance between any two terminal devices 16 present in a certain area at predetermined time intervals (for example, every second) during a predetermined length of time (for example, 3 minutes), It is a value obtained by calculating their average.

  The group forming unit 40 applies a rule to the data when the average distance between the terminals is equal to or less than 10 m (first threshold value). As a result, the first terminal device 16a to the fifth terminal device 16e belong to the first group, the sixth terminal device 16f and the seventh terminal device 16g belong to the second group, and the eighth terminal device 16h belongs to the third group. . Note that the group forming unit 40 sets a minimum value of the number of terminals forming one group, and does not have to form a group when the number is less than the number. For example, when the condition of at least three terminal devices 16 is applied, only the first group is formed in the data example shown in FIG. Further, the group forming unit 40 may set the maximum value of the number of terminals forming one group. Also, the group forming unit 40 may calculate the maximum value of the distance, instead of the average value of the distance between the terminals in the predetermined period, and form a group using the maximum value. Further, the group forming unit 40 may calculate a variance or a standard deviation indicating the magnitude of the fluctuation of the distance between the terminals, and form a group using the variance or the standard deviation.

  For example, when the average value of the distance between the terminals in the predetermined period is equal to or less than the first threshold value and the standard deviation of the distance between the terminals is equal to or less than the third threshold value, The group may be determined. By using the standard deviation, the probability of erroneously recognizing the terminal device 16 that is not performing a group action as a group is reduced. In addition, when the number of candidate terminals forming one group is large to some extent, the group forming unit 40 sets the first threshold value even if the distance between group candidate terminals exceeds the first threshold value. If the number of exceeding distances is equal to or less than a predetermined number and the maximum value of the distance is equal to or less than a second threshold value, processing such as grouping may be executed.

  For example, in the group determination process, when the number of terminal devices 16 that are candidates for a certain group is equal to or more than a predetermined number (for example, seven), the following process is performed. Here, there are 10 terminal devices 16 that are candidates for a certain group, and the distance between one terminal device 16 (hereinafter, referred to as “determination target terminal”) and the other nine terminal devices 16 is set. Is the maximum value of 18 m, the second largest distance is 10 m, and the maximum value of the distance between the other nine terminal devices 16 is 9 m. In this case, the number of distances exceeding the first threshold value (for example, 10 m) for group determination is one, this number is equal to or less than a predetermined number (for example, two), and the maximum distance value (18 m) Is less than or equal to the second threshold value (for example, 20 m), the group forming unit 40 determines that the determination target terminal also belongs to the group. Note that the group determination process is not limited to the above-described method. For example, the group forming unit 40 applies various known cluster analysis methods to the distance between terminals, and associates the formed clusters with the groups. It may be performed using a process such as causing

  The group forming unit 40 generates a new group ID each time one group is formed, and stores the new group ID in the storage unit in the group information processing unit 24 as a database shown in FIG. FIG. 7 shows a data structure of a database stored in the group information processing unit 24. The database associates a group ID, a terminal ID (user ID), and a leader point (Rp), and is also called a group information table. The leader point is a numerical value obtained by estimating the influence of the user in the group based on the behavior of the user in the group. The greater the value, the greater the influence of the user (terminal device 16). Although not shown in FIG. 7, the date and time when the group was formed (group start date and time) may be recorded in the database. Furthermore, the date and time when the group was canceled (group end date and time) may be recorded in the database. For example, the group ID, the terminal ID (user ID), the leader point (Rp), the group start date and time, and the group end date and time may be stored as a group information table in association with each other.

  FIG. 7 shows a state in which a group having a group ID “G1” and a group having a group ID “G2” are formed. The example shown in this figure shows a state in which “G2” is formed after “G1” is formed and resolved. The group with the group ID “G1” includes a first terminal device 16a, a second terminal device 16b, and a third terminal device 16c. The respective leader points are “20”, “50”, and “30”. The leader point calculation method will be described later. The group with the group ID “G2” includes the first terminal device 16a, the third terminal device 16c, the sixth terminal device 16f, and the seventh terminal device 16g, and is an initial state in which the group is formed. As described above, in the initial state in which the group is formed, the leader point of each user is “0”. Note that the leader point stored in the group information table is updated as needed until one group action ends. Furthermore, after one group action is completed, it is stored in the same value state without being updated. The term "temporary point" may be used to represent a leader point that is in an updated state.

  The point calculation unit 42 estimates the influence of the users in the group on the decision making using the time-series data of the position information stored in the position information management unit 22. Specifically, in step S110, the point calculation unit 42 calculates an influence value (leader point) as an index indicating the influence of each user. In other words, the point calculation unit 42 analyzes the time-series data of the position information of each terminal ID, and determines, for each of a plurality of subjects included in the group, a predetermined effect that the influence of the subject in the group is high. A characteristic action amount indicating the degree of matching with the action pattern is calculated. Further, the point calculation unit 42 calculates or updates a leader point (temporary point) for each subject based on the characteristic action amount. Hereinafter, this processing will be described in detail with reference to the flowchart of FIG.

  The point calculation unit 42 calculates a leader point such that the higher the rank determined by the position of the subject, the higher the value. Specifically, in step S200, the point calculation unit 42 defines that the subject is located in a predetermined order (order) from the head of the group based on the moving direction of the group as a first predetermined action pattern. Then, the number of times the subject is positioned in a predetermined order is calculated as the characteristic action amount. Typically, the point calculation unit 42 counts the user's preceding frequency. The precedence frequency is the number of times that the user is located at the head of the group with reference to the moving direction of the group.

  The outline of the process in step S200 will be described with reference to FIG. In the example shown in this figure, the first terminal device 16a, the second terminal device 16b, and the third terminal device 16c are moving in the direction of the arrow. The point calculation unit 42 compares the terminal ID and the latitude / longitude information stored in the position information management unit 22 with, for example, the latest predetermined number (for example, 100 times) of latitude / longitude information from the latest, so that the group Specifies the user located at the head of the traveling direction. In addition, the point calculation unit 42 counts the number of times (preceding frequency) at the preceding position for each terminal ID. Further, the point calculation unit 42 calculates an influence value (leader point) indicating the influence of the user using the preceding frequency.

  For example, among the position information measured 100 times, it is assumed that the number of times the third terminal device 16c, the first terminal device 16a, and the second terminal device 16b are at the top is 60 times, 30 times, and 10 times, respectively. In that case, the leader points of the third terminal device 16c, the first terminal device 16a, and the second terminal device 16b are respectively "60", "30", and "10". Alternatively, when the position information of each terminal device 16 is updated every predetermined time (for example, every 5 seconds), the terminal device 16 located at the head of the group is specified at each update timing, and its leader point is set to a predetermined value. Processing such as increasing the number (for example, “1”) may be performed.

  As described above, the characteristic action amount may be used as it is as a leader point, or a value obtained by multiplying the characteristic action amount by a predetermined coefficient may be used as the leader point. Such processing is based on the finding that when a person moves in a group, the user who walks at the head of the group tends to have the greatest influence. In particular, when the group is moving while searching for an appropriate store for a certain purpose, such a tendency is likely to appear more remarkably. The leader point can be said to be an index indicating the influence of the group user when searching and selecting an item such as a store or a product. The point calculation unit 42 adds the calculated leader point (leader point related to the leading frequency) to the leader point of the group information table, and updates the group information table.

  Note that the point calculation unit 42 may count the number of times the user is positioned in a predetermined order, such as the second from the top or the third from the top, without being limited to the case where the user is positioned at the top. Further, the point calculation unit 42 may calculate the characteristic action amount such that the higher the rank of the position from the top, the larger the value. That is, the point calculation unit 42 may set the leader point to a larger value as the position ranking from the head is higher. This corresponds to adding a leader point whose value has been changed according to the rank (order) from the top. For example, if it is located at the second position from the head, a leader point such as 50% of the position at the top is added, and if it is located at the third position from the beginning, a leader point such as 20% is added.

  Further, the point calculation unit 42 may determine the position ranking from the head used for the leader point calculation according to the number of subjects included in the group. For example, when the number of groups is small (less than the first predetermined number), only the first position from the beginning is considered, and when the number of groups is medium (the first predetermined number or more, and If the number is less than the second predetermined number), the first to second positions are considered. When the number of groups is large (when the number is equal to or more than the second predetermined number), the third position from the top is considered.

  The point calculation unit 42 may calculate the leader point using the distance moved at the head of the group instead of the preceding frequency as the characteristic action amount and using the characteristic action amount. For example, within a predetermined time, the first terminal device 16a moves 500 m with the first position, the second terminal device 16b moves 300 m with the first position, and the third terminal device 16c is the first position. It is assumed that the user has moved 100 m with. In this situation, assuming that the moving distance in meters is the characteristic action amount, the characteristic action amounts are “500”, “300”, and “100”. The point calculation unit 42 may set a value obtained by multiplying the characteristic action amount by “0.1” as a leader point, and calculate each leader point as “50”, “30”, or “10”. That is, the point calculation unit 42 may calculate the leader point such that the longer the distance moved to the head, the larger the value.

  The point calculation unit 42 may calculate the leader point using the length of time during which each terminal device 16 is located at the head of the group as the characteristic action amount, and using the characteristic action amount. For example, in 10 minutes (predetermined time), the time at which the first terminal device 16a was at the head is 2 minutes, the time at which the second terminal device 16b was at the head is 7 minutes, and It is assumed that the time at which the terminal device 16c is located at the top is one minute. In this situation, if the time in minutes is a feature amount, the feature action amounts are “2”, “7”, and “1”. The point calculation unit 42 may set a value obtained by multiplying the amount of characteristic action by “10” as a leader point, and calculate each leader point as “20”, “70”, or “10”. That is, the point calculation unit 42 may calculate the leader point such that the longer the time at the head is, the larger the value is. Furthermore, the point calculation unit 42 may calculate the leader point by arbitrarily combining the characteristic action amounts relating to the number of times, the time, and the distance located at the head of the group.

  Next, in step S210, when one group is divided into a plurality of groups and then merges, the point calculation unit 42 determines a pattern in which the subject has moved from the time of division in the direction in which the group moves after the merge into a second predetermined behavior pattern. And the number of times of merging after division is calculated as the characteristic action amount. At this time, the point calculation unit 42 determines whether the small groups (including one user) have joined or joined based on the moving direction of the group after the joining. In other words, the point calculation unit 42 determines that the small group that has moved in the same direction after the merge from the time of division is the merged side, and adds the leader points of the users of the merged small group.

  Here, when a plurality of terminal devices 16 that have been grouped take different actions and then join the group again, the terminal ID that precedes the merged group has an influence on the group. Therefore, the point calculation unit 42 adds the leader point to this. FIGS. 10A and 10B show an outline of another processing in the point calculation unit 42. In FIG. 10A, the first terminal device 16a, the second terminal device 16b, and the third terminal device 16c move in different directions. In FIG. 10B, following FIG. 10A, the first terminal device 16a and the second terminal device 16b join the third terminal device 16c, and the three terminal devices 16 move in the same direction. . When detecting such an action pattern, the point calculation unit 42 sets a leader value of the merged terminal device 16 (the third terminal device 16c in this example) to a predetermined value (for example, “30”) for each merge. Is added.

  That is, the point calculation unit 42 updates the group information table by adding the leader point related to the merging action to the leader point of the group information table. If the small group on the merged side is composed of a plurality of users, the point calculation unit 42 may add the leader points for all of them, or may add the leader point to a predetermined ranking (for example, The addition may be limited to the first and second users. Further, the point calculation unit 42 may change the leader points to be added according to the order from the head in the small group on the merged side. For example, the point calculation unit 42 adds leader points such as “30” to the first user, “20” to the second user from the top, and “10” to the third user from the top.

  The point calculation unit 42 may calculate the leader point based on the number of subjects included in the small group when the group is divided into a plurality. That is, the point calculation unit 42 may calculate the value of the leader point related to the merging action according to the number of users who have taken another action (the number of users who have joined). Specifically, the point calculation unit 42 calculates the value of the leader point related to the merging action such that the larger the number of persons who have taken another action (the larger the number of small groups that have joined), the larger the value. . For example, the point calculation unit 42 has “20” when two people join after taking another action, and “30” when three people join after taking another action, and four people took another action. If they join later, a leader point such as "40" is added.

  The point calculation unit 42 may calculate the value of the leader point related to the merging action according to the number of persons on the merging side. Specifically, the point calculation unit 42 calculates a leader point having a smaller value as the number of persons on the merged side increases, and adds the leader point to each user on the merged side. For example, the point calculation unit 42 determines “50” when the number of persons on the merging side is one, “30” when the number of persons is two, and “3” when three persons are three. Each person adds a leader point such as "10". When there are a plurality of users on the merging side, the point calculation unit 42 may add the leader point related to the merging action to the first user among them. That is, the point calculation unit 42 may calculate a leader point having a smaller value as the number of persons on the merging side increases, and may add the leader point related to the merging action to the user located at the head. For example, the point calculation unit 42 determines that “50” when the number of persons on the merging side is one, “40” when two persons are joined, and “40” when the number of persons joined is three. Alternatively, a leader point such as "30" may be added to the person located at the top of the list.

  Next, in step S220, when the subject stops, the point calculation unit 42 defines a pattern in which other subjects also stop following the stop as a third predetermined action pattern, and determines the number of times of movement stop as a characteristic action. Calculate as quantity. The stop following the stop means that immediately after a certain terminal device 16 (for example, the first terminal device 16a) stops moving, another terminal device 16 (a terminal device 16 other than the first terminal device 16a in the group) is stopped. This is the situation where the movement has been stopped. This situation is likely to be a situation in which the user who has stopped moving first finds a store or the like that he is interested in, stops there, and is considering entering the store. For this reason, the point calculation unit 42 adds a predetermined value (for example, “20”) to the leader point of the terminal device 16 that has stopped moving first for each stop of movement of another terminal device 16.

  That is, the leader point related to the stopping action is added to the leader point of the group information table, and the group information table is updated. However, when a certain terminal device 16 stops moving but another terminal device 16 does not stop moving and continues moving, it can be determined that the influence of the user who first stopped moving is not so high. Therefore, leader points are not added.

  The point calculation unit 42 may calculate the leader point based on the number of other subjects that have stopped following the stop. Specifically, the point calculation unit 42 calculates a leader point having a larger value as the number of persons who stop later increases. For example, the point calculation unit 42 adds leader points such as “20” when two people stop, “30” when three people stop, and “40” when four people stop. In addition, when there are a mixture of users who have stopped afterwards and users who have continued moving without stopping, the point calculation unit 42 calculates a leader point related to the stopping action according to the proportion of users who have stopped afterwards. It may be calculated. For example, when the number of stopped users is Ns and the number of continuously moving users is Nc, the point calculation unit 42 calculates the ratio of the number of stopped users as Ns ÷ (Ns + Nc). Further, the point calculation unit 42 sets “40” when the ratio is 80% or more, “30” when the ratio is 60% or more and less than 80%, “20” when the ratio is 40% or more and less than 60%, If it is less than 40%, a leader point such as "0" is added. As described above, the point calculation unit 42 may calculate the leader point having a larger value as the ratio of the number of stopped persons increases.

  The point calculation unit 42 determines whether or not the time-series data of the position information of each terminal device 16 matches at least one of the first to third predetermined behavior patterns. Alternatively, in addition to the deterministic determination, the determination may be made stochastically. In that case, the point calculation unit 42 uses a time-series data of the position information as an input, and uses a probability model that outputs a probability value corresponding to a predetermined behavior pattern of each terminal device 16.

  For example, in the processing for the first predetermined action pattern in step S200, the point calculation unit 42 determines that the two terminal devices 16 from the head of the group are substantially side by side during a predetermined period (for example, one minute). The probability that the first terminal device 16a is the first place is calculated as “0.75” or the like. At that time, the point calculation unit 42 calculates the probability that the second terminal device 16b is the first place, such as “0.25”. The case where they are substantially horizontally arranged is, for example, a case where it is not clear which is the first place, a case where the first place and the second place of the group are frequently switched within a short time, and the state is not stable.

  In addition, for example, in the process for the third predetermined action pattern in step S220, the point calculation unit 42 determines that the first terminal device 16a stops moving and the other terminal devices 16 stop moving even if the moving speed is significantly reduced. If the vehicle moves forward slowly without performing, the probability of matching with the third predetermined action pattern is calculated as “0.2” or the like. The point calculation unit 42 may use the probability value thus calculated as it is as the characteristic action amount, or may use a value obtained by multiplying the probability value by a predetermined coefficient (for example, 100 times) as the characteristic action amount.

  As described above, the characteristic action amount is a degree (time, time, time, etc.) in which the time-series data of the position information of a certain subject matches a predetermined behavior pattern in which the influence of the subject in the group is determined to be high. Distance, probability). Furthermore, the leader point is the sum total of the characteristic action amount of each subject during a predetermined period (for example, from the time of group formation to the time of group dissolution), so that the larger (larger) the characteristic action amount, the higher (larger) the leader point. Have the property of becoming. The above is the description of the leader point update processing.

  Returning to the description of the flowchart of FIG. In step S120, the point calculation unit 42 determines a leader point. This process is performed every time one group action is completed. That is, the leader point after this processing is executed can be said to be an index indicating the influence of the member in one group action. Typically, a group action is completed by entering a store at a certain group. The point calculation unit 42 refers to the store information table in the store information storage unit 28 shown in FIG. 4, and when the terminal device 16 belonging to the group stays at a position corresponding to the latitude and longitude of the store for a certain time or more, It is determined that the group has entered the store. Then, the processing is performed with the group entry as a trigger. Further, when the group enters a restaurant, for example, the fact that the member sits down (the position information of the member is not changed for a certain period of time or longer) may be used as a trigger to perform this processing. Further, this processing may be performed when the group action is canceled and each member moves to the single action.

  The first method of determining the leader point is a method of using the leader points accumulated up to that point as it is. That is, in the group information table of the group information processing unit 24 shown in FIG. 7, the row of the corresponding group ID is not updated thereafter. It can be said that this method quantifies the influence of each member in the group as an absolute value. The action history can be quantified in more detail.

  The second method of determining the leader point is a method of normalizing the total sum of the leader points of each member in one group action so as to be a constant value. Specifically, the leader points of the corresponding group ID are read from the group information table, and the leader points of each member are normalized so that the sum of the leader points is "1.0" or "100". For example, when the group G1 is composed of three terminal devices 16 of A, B, and C, and their leader points are “20”, “50”, and “30”, the total is “1.0”. Are normalized such as “0.2”, “0.5”, “0.3”. Alternatively, the leader point of the member having the largest leader point in one group may be normalized such that the leader point becomes a predetermined value (eg, “1.0”). Such normalized (relative) leader points indicate the relative influence of each member in one group action, and are intuitive and easy to understand.

  In the following description, it is assumed that the leader point has been determined using the second method, unless otherwise specified. However, the first method can be used in the same manner by executing some processing each time. The group information processing unit 24 stores the determined leader points in the point history table 44 in the tally processing unit 26. As shown in FIG. 11, the point history table 44 stores point history data (point history) in which a group ID, a terminal ID, a calculation date (entry date and time), a leader point, and a store ID are associated. Table 44).

  In step S120, a leader point adding process may be further performed. Specifically, the same processing as in steps S200 to S220 is performed on the group's action history within a predetermined distance from the position of the store where the group has entered, and after adding the leader point, the normalization processing is performed. May be performed. By the processing in steps S200 to S220, the action history near (near) the store that has entered the store has already been reflected in the leader point, but the processing in steps S200 to S220 is constant regardless of the distance from the store. Leader points are added on condition. However, the action history near (near) the store that entered the store may have a more prominent influence on the members in the group related to the selection of the store than the action history in an area far from the store. high. Therefore, by reflecting the action history in the area near the store more strongly on the leader point, the accuracy of the leader point can be improved.

  Therefore, for example, in the case where the first terminal device 16a is positioned “8” times and the second terminal device 16b is positioned “2” times within 100 m of the store where the group has entered, the processing corresponding to step S200 is performed. The process is executed, and the leader point is added by “8” to the first terminal device 16a and “2” to the second terminal device 16b. In the leader point adding process in this nearby area, the same condition as in step S200 may be executed again, or a different condition may be set. For example, in step S200, if "1" points are given for one preceding frequency, the addition may be performed under the same condition in the nearby area, or "2" points or "0. Conditions may be changed, such as 5 "points. Similarly, in steps S210 and S220, a leader point adding process may be performed in the area near the store.

  Next, the operation of the aggregation processing unit 26 will be described with reference to the flowchart of FIG. FIG. 12 is a flowchart illustrating a procedure for counting leader points by the information providing apparatus 10. The control unit 30 gives an operation instruction to the aggregation processing unit 26 at a predetermined timing. As the predetermined timing, for example, the counting processing unit 26 is operated in a batch process at a time interval such as once a day, once an hour, or the like. Alternatively, the control unit 30 may operate the tally processing unit 26 with a predetermined timing when a store operator or a shopping mall operator performs a predetermined operation from the management client 18 as a trigger.

  First, in step S300, the tallying processing unit 26 specifies a leader point to be tallyed. The first specific method is a method of using the aggregation period designated by the operator using the management client 18. This method is suitable for executing a tallying process using a predetermined operation of the operator as a trigger. The operator specifies the totalization start date and time and the totalization start date and time, and transmits the information from the management client 18 to the information providing apparatus 10.

  The second specifying method is a method in which the current date and time at which the process is executed is set as the counting end date and time, and a period that is a predetermined period from the current date and time is set as the counting start date and time. This method is suitable for executing a totaling process as a batch process at a predetermined date and time. For example, if the counting start date and time is “January 1, 2015 at 0:00:00” and the counting end date and time is “January 31, 2015 at 8:59:59”, the tallying processing unit 26 Then, the point history table 44 shown in FIG. 11 is scanned, and data whose calculation date and time is after the count start date and time and before the count end date and time is selected, and is set as the count target data.

  Next, in step S310, the tallying processing unit 26 tallies up the tallying target data for each store ID. Specifically, using a predetermined rule (conversion rule), a category is determined in accordance with the size of the leader point, and the number of people for each category is counted. For example, when the value of the leader point (Rp) of each data to be tabulated is “0.6 or more”, it is made to correspond to the category of “Rp large”, and when it is “0.4 or more and less than 0.6”, The category is made to correspond to the category of "Rp medium", and the case of "less than 0.4" is made to correspond to the category of "Rp small". Alternatively, when the leader point is calculated by the first method in step S120 (when it is not normalized), for example, when the value of Rp is “120 or more”, it is made to correspond to the category of “Rp large”. . Further, the case of “50 or more and less than 120” corresponds to the category of “Rp medium”, and the case of “less than 50” corresponds to the category of “Rp small”. Then, the number of terminal IDs (equivalent to the number of users) corresponding to each of the three categories is counted for each store ID. Hereinafter, this category is referred to as a “leader point category” or an “Rp category”.

  That is, the tallying processing unit 26 calculates the leader points for each item by counting the leader points calculated by the point calculation unit 42. This is also called an item point. At this time, a category according to the size of the leader point is defined, and the aggregation processing unit 26 aggregates the leader points for each item for each category, thereby distributing the category-based distribution of the leader points for each item as the item points. calculate.

  In this embodiment, there are three types of Rp categories, but of course, other numbers of categories may be used. For example, in the example illustrated in FIG. 11, the group ID corresponding to the store ID “S1” is two of “G1” and “G7”, and the leader point corresponding to “G1” is “0.2”. , “0.5” and “0.3”, and the leader points corresponding to “G7” are “0.9” and “0.1”. When these five leader points are made to correspond to categories based on the above criteria, “Rp large = 1”, “Rp medium = 1”, and “Rp small = 3”. A combination of these three pieces of information becomes an item point, but only a part of the information (for example, “large Rp = 1”) may be used as an item point.

  Further, the classification criteria (classification conditions) of the Rp category may be changed according to the number of people in the group. In a case where the sum of group points is normalized to be “1” in one group, the average value (expected value) of leader points per person decreases as the number of groups increases. For this reason, when the number of groups is small, the condition for increasing Rp is strict (the threshold value corresponding to large Rp is increased), and when the number of groups is large, the condition for increasing Rp is relaxed ( The threshold value corresponding to the large Rp is reduced). For example, the criteria shown in FIG. 13 can be used. FIG. 13 shows the data structure of the database stored in the tallying unit 26. In the example of this figure, when the number of groups is “2”, if the leader point (Rp) is “0.8 or more”, it is made to correspond to “Rp large” and “0.4 or more and less than 0.8”. Indicates that it corresponds to “medium Rp”, and “less than 0.4” indicates that it corresponds to “low Rp”.

  In step S310, a statistical value such as a representative value of a leader point for a certain item or a value indicating a degree of variation may be calculated as an item point. Any of the average value, the median value, and the mode value can be used as the representative value. Standard deviation, variance, (maximum value-minimum value), quartile range (third quartile-first quartile), and the like can be used as the value indicating the degree of variation of the leader point. Specifically, the tallying processing unit 26 targets the tallying data, which is at least a part of the point history table 44 shown in FIG. 11, for each store ID, a representative value (for example, an average value) of leader points, and What is necessary is just to calculate the value (for example, standard deviation) which shows the degree of variation of a leader point. For example, in the example shown in FIG. 11, the average value of the five data corresponding to the store ID “S3” is “(0.1 + 0.3 + 0.5 + 0.05 + 0.05) ÷ 5 = 0.2”. . Further, the standard deviation is approximately “0.18”.

  Next, in step S320, the tallying processing unit 26 stores the data tallyed in step S310 in the store information storage unit 28. The data calculated in step S310 is stored in the “Rp large”, “Rp medium”, and “Rp small” columns of the store information table shown in FIG. In the example shown in this figure, the number of visitors per day and the number distribution of each Rp category are stored. Although not shown in the figure, when the representative value of the leader point and the value indicating the degree of variation of a certain item are calculated in step S310, they are also stored. Further, information on a price range of a product provided at a store may be stored.

  Next, in step S330, the aggregation processing unit 26 creates display data to be displayed on the management client 18 using the data stored in the store information storage unit 28. For example, when the management client 18 performs display using a Web browser, display data for displaying a table, a graph, or the like is created using HTML or a script language.

  Next, in step S340, the control unit 30 transmits the display data created in step S330 to the management client 18 via the transmission / reception unit 20. For example, a request from a Web browser installed in the management client 18 may be received using a protocol such as HTTP, and display data may be transmitted as a response. Further, the mail may be transmitted using a protocol such as SMTP. Thus, the transmission / reception unit 20 outputs the leader point calculated by the tally processing unit 26.

  The management client 18 is configured to be able to communicate with the information providing apparatus 10 as described above. A receiving unit (not shown) in the management client 18 receives display data indicating a leader point from the information providing device 10. Further, a control unit (not shown) in the management client 18 performs control to display the received data on the display unit. Therefore, upon receiving the display data from the information providing device 10, the management client 18 displays information related to the leader point on the display unit based on the display data.

  FIG. 14 shows information displayed on the management client 18, which is an example of display of item point information on the management client 18. This figure is a result of analyzing customers who visited the stores S11 to S14 during the predetermined period, and shows the number of people by Rp category and the total number of visitors in a table format. In the example shown in the figure, the store S11 and the store S12 both have a total of 18 visitors, but the store S11 has the largest number of “Rp large” and the smallest number of “Rp small”. On the other hand, in the store S12, in contrast, the number of “Rp large” is the smallest, and the number of “Rp small” is the largest. It can be said that "Rp large" people often act positively when selecting a store.

  In addition, in selecting a store, it can be said that the influence on other customers is relatively high. In addition, it can be said that there is much knowledge about the store, and the store is often determined in advance for the purpose. Such a person is likely to bring a friend / acquaintance or the like to the store or to introduce the store to a friend / acquaintance or the like in the future. That is, a store with many "Rp large" visitors is likely to have more new customers in the future. In the example shown in the figure, even if the store S11 and the store S12 have the same number of visitors, the store S11 can be expected to have more visitors in the future and to be promising. Store operators and shopping mall operators can easily view such distribution information of leader points for each store, such as renovating stores, increasing floor space, purchasing plans, and changing store layouts in shopping malls. Can be more accurately and accurately determined.

  FIG. 15 shows another information displayed on the management client 18, which is a graph of the data shown in FIG. Compared with FIG. 14, it is possible to more intuitively grasp the difference in the property for each store. For example, in the store S14, although the absolute number of visitors is small at present, the ratio of “Rp large” is large, and thus it can be expected that the number of visitors will increase in the future.

  FIG. 16 is a layout diagram of stores in a shopping mall to be processed by the information providing system 100, and the number of visitors for each Rp category in a specific period is described at each store location. In the example of this figure, it can be seen that the stores where the number of Rp-large visitors is 100 or more are biased toward S1, S4, and S5. By providing such information, the shopping mall operator has stated, “One of the reasons why the number of Rp-large visitors is relatively small in the stores S2, S3, S6, and S8 is the location of the stores. There is a possibility. " Further, the operator can make a judgment such as “S3 and S5 are swapped, so that the movement route of the shopper changes, so that an increase in the number of shoppers at S3 and nearby shops can be expected”.

  FIG. 17 is a graph showing still another information displayed on the management client 18 and showing a visitor ratio for each store by Rp category. The three stores S1, S5, and S10 have a relatively high ratio of “Rp large” visitors, and it can be determined that there is a high possibility that new visitors will increase in the future. In addition, since these three stores have a common property that they are favored by people who actively take action when selecting a store, it is possible to determine that a campaign or the like in which the three stores cooperate is also effective. For example, when the store S1 is crowded, measures such as introducing the store S5 or S10 to a visitor and guiding them, or issuing coupons that can be used among the three stores can be considered. In addition, a customer (user) who provides a coupon or a special treatment service may be selected based on the leader point. For example, a coupon may be provided for a customer who has come to the store S1 and whose leader point satisfies a specific condition. For example, a coupon may be provided for a customer of “Rp large” or a customer with a leader point of “0.6” or more. A customer with a high leader point is highly likely to introduce the shop to friends, acquaintances, and the like of the customer, so that an effect of increasing more new customers can be expected, and efficient promotion activities can be performed.

  FIGS. 18A and 18B show still other information displayed on the management client 18, and these are another example of displaying leader point distribution information. Here, FIG. 18A shows a graph display, and FIG. 18B shows a table display. In order to perform such a display, in the store information table shown in FIG. 4, the number of persons may be totaled for each genre and for each Rp category. FIGS. 18A and 18B show the distribution of the leader points according to the genre of food, not for each store, but for stores located in a specific area such as a shopping mall. In the figure, in the three genres “Japanese food”, “French”, and “ethnic”, the ratio of “large Rp” is high. For this reason, it is expected that these three genres are relatively likely to increase new customers in the future. The shopping mall operator can utilize such information for future business planning, such as increasing the number of stores in these three genres. Campaigns such as mutually guiding customers at stores of these three genres are also considered effective.

  FIGS. 19A and 19B show still another information displayed on the management client 18, which compiles a plurality of stores and distributes leader point distribution information for each price range of products handled by the stores. Is an example of displaying. Here, FIG. 19A shows a graph display, and FIG. 19B shows a table display. This price range indicates, for example, a central price range of a restaurant menu. In this figure, the ratio of “Rp large” is higher in stores that handle products in the price range of “2,500 yen to 10000 yen” than in stores that handle products in other price ranges. Therefore, the shopping mall operator can determine that increasing the number of stores that handle products in the price range of “2,500 yen to 10,000 yen” may lead to an increase in sales of the shopping mall.

  After the group enters the store, the location information of the terminal device 16 in the store may be collected and provided to the management client 18. For example, the position information of the terminal device 16 in the store is continuously recorded in the position information table shown in FIG. Then, data corresponding to the store where the group has entered is extracted from the point history table 44 and is combined with the position information table using the terminal ID as a key, thereby obtaining the position information of the terminal device 16 and the leader point of the terminal device 16. Can be associated. In this case, the tallying process in the tallying processing unit 26 may be omitted, and only the leader point related to the current entry (the leader point in one group action) may be associated. Alternatively, the tallying processing unit 26 may calculate and associate the leader point representative value (average value, median value, mode value, etc.) of the terminal device 16 in the past week or past month.

  The aggregation processing unit 26 creates display data for displaying the leader point of each terminal device 16 on the in-store map. Then, the control unit 30 transmits the display data to the management client 18 via the transmission / reception unit 20. By viewing such information, the clerk of the store where the group has entered the store can know who the customers currently in the store have and how many leader points. Further, it is possible to improve customer service and improve efficiency, for example, by giving a menu or wine list to a person with a high leader point preferentially.

  In the present embodiment, the method of calculating the leader point using the latitude and longitude information of the terminal device 16 stored in the position information management unit 22 has been described, but another method may be used. For example, the processing corresponding to steps S100 and S110 may be performed by analyzing the image / video of a monitoring camera installed in a shopping mall or a shopping street, and using an image recognition technique. In this case, the user does not need to carry around the terminal device 16. Further, the latitude and longitude information is not necessarily required, and it is sufficient that the relative positional relationship and relative movement information of the members in the group can be acquired. In addition, by using surveillance cameras installed in the aisles of shopping malls and shopping streets and surveillance cameras installed at the entrance of the store and using face recognition technology, the leader point of any person who enters the store is high. The clerk can know which person has a lower leader point. By knowing such information, it is possible to improve customer service and improve efficiency, for example, by giving a menu or wine list preferentially to a person with a high leader point.

  Further, a group action history in a store may be acquired, and the influence on product selection may be estimated. For example, a member who first arrives at a shelf of a product purchased by a member is specified, and a process such as adding a leader point of the member is performed to calculate a leader point (influence value) related to product selection. Is also good. That is, according to the invention of the present embodiment, it is possible to calculate the leader point (influence value) related to the selection (selection) of an item including a product, not limited to the case where the selection target is a store.

  Further, after calculating the leader points related to the product selection, the number of purchasers for each leader point category for each product may be totaled. For example, the display for each store shown in FIG. 15 may be changed and displayed for each product. By providing such information, the store operator can make predictions such as "a product having a large ratio of Rp has a high possibility of increasing demand in the future".

  In the above description, the tallying processing unit 26 calculates the leader points for each item by counting the leader points calculated by the point calculation unit 42. However, another method may be used. For example, a leader point related to a specific item is extracted from the point history table 44 shown in FIG. 11, and a list of the extracted leader points is stored in the management client 18 in association with the item (information related to the item). It may be provided and displayed. For example, in the example shown in FIG. 11, the leader points corresponding to the store ID “S1” are “0.2”, “0.5”, “0.3”, “0.9”, “0.1”. , But the list of the five leader points may be displayed in association with the store “S1”. In this case, the tally processing unit 26 can be omitted. Note that as the information on the item, a store ID, a store name, a store address, a store telephone number, and the like can be used. In cases such as when the number of leader points associated with an item is small, providing raw data that is not aggregated may be more useful to store operators than information that aggregates leader points. It is preferable to provide the information of the individual leader points stored in the point history table 44 in this way.

  According to the present embodiment, since a group is formed based on the time-series data of the position information, it is possible to group a plurality of subjects who act together. Further, since the grouping is automatically performed, the convenience for the user can be improved. In addition, based on the time-series data of the position information, a leader action is calculated after calculating a characteristic action amount indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high. Leader points can be derived in consideration of the influence of the users within the group.

  The store operator provides information (item points) that summarizes the leader points that estimate the influence of members in the group related to the selection of items (stores or products) for each item without placing a burden on customers and store operators, etc. Etc. can be provided. As a result, it is possible to clearly convey the properties of each item to a store operator or the like from a new perspective that has not been provided before. For example, it is possible to provide information useful for forecasting demand such as "Items used by customers with high leader points are expected to further increase demand in the future." Also, sales such as "customers with high leader points are presumed to have a strong influence on others, so it is efficient to conduct promotions such as promotions and special offers targeting customers with high leader points." Can provide information useful for promotion strategies. Also, "stores with similar leader point distribution patterns can be determined to have similar customer demographics, so customers can be mutually guided among such stores, or campaigns that link multiple stores can be implemented. Then, it is effective to increase the sales of the whole area. " In addition, it is possible to provide information useful for improving and improving customer service, such as "Who is the customer who has the highest leader point in the customer group who has entered the store?"

  Further, at least one of the number of times, the matching time, the matching moving distance, and the matching probability of the movement pattern of the subject in the time-series data of the position information matches the predetermined action pattern is calculated as the characteristic action amount. Therefore, it is possible to derive a characteristic action amount that matches the degree of matching of the action pattern. In addition, since the leader point is calculated so as to have a higher value as the characteristic action amount increases, a leader point corresponding to the degree of matching of the action pattern can be derived. Further, the predetermined action pattern is a pattern in which the subject is located at a position of a predetermined order from the head of the group with reference to the moving direction of the group, so that the influence of the user according to the position where the user in the group is present Can be estimated with high accuracy. Further, since the leader point is calculated so as to be higher as the predetermined rank is higher, it can be estimated that the influence of the user existing at the head of the group is larger.

  In addition, the predetermined action pattern is a pattern in which, when a group is divided into a plurality of groups and then merges, the main body moves from the time of division in a direction in which the group moves after merging, so that the influence of the user is estimated with high accuracy. it can. In addition, since the leader point is calculated based on the number of subjects included in the small group when the group is divided into a plurality, the magnitude of the influence can be estimated with high accuracy. In addition, the predetermined behavior pattern is a pattern in which, when the subject stops, the other subjects also stop following the stop, so that the influence when the subject stops can be estimated. In addition, since the leader point is calculated based on the number of other subjects that have stopped following the stop, the magnitude of the influence can be estimated with high accuracy. Further, since the leader point is calculated according to the number of subjects included in the group, the magnitude of the influence can be estimated with high accuracy.

(Example 2)
Next, a second embodiment will be described. The second embodiment relates to an information providing system as in the first embodiment. The information providing apparatus 10 according to the second embodiment also forms a group using the position information of the plurality of terminal devices 16 and estimates the influence of each user in the group, but estimates the influence of each user. At this time, the attribute information of the user is also used. The information providing system 100 according to the second embodiment is of the same type as that of FIG. Here, the difference will be mainly described.

  FIG. 20 illustrates a configuration of the information providing apparatus 10 according to the second embodiment of the present invention. The information providing device 10 is configured in the same manner as in FIG. 2, but the group information processing unit 24 is configured to be able to refer to the database stored in the user information storage unit 32. The point calculation unit 42 calculates the leader point by estimating the influence within the group using not only the position information but also the attribute information (age, sex, etc.) of the user corresponding to the subject. The user information storage unit 32 stores user attribute information.

  FIG. 21 shows a data structure of a database stored in the user information storage unit 32. The terminal ID (or user ID) is stored in association with user attribute items such as date of birth, gender, office, post, annual income, and extra money this month. This is an example, and data of other user attribute items may be stored, or only some of the attribute items may be stored. Referring back to FIG.

  The point calculation unit 42 according to the first embodiment calculates the leader point based on the position information. In addition, the point calculation unit 42 according to the second embodiment calculates the leader point based on the user attribute information. Further, the point calculation unit 42 calculates an overall leader point using the two leader points. Hereinafter, the leader point based on the position information of the terminal device 16i is Pg [i], the leader point based on the user attribute information is Ph [i], and the comprehensive leader point is P [i]. The point calculation unit 42 calculates the leader point Ph [i] based on the user attribute information by the following method.

  The first method is a method of calculating based on rules set for each user attribute item. For example, generally, since the influence of a user with a high age is high, Ph [i] can be calculated based on the date of birth. Specifically, the point calculation unit 42 gives a leader point of a larger value as the age is higher, based on the birth dates of the users in the group. For example, the point calculation unit 42 gives a leader point of “10” to a user with the highest age, “7” to a user with the second highest age, and “5” to a user with the third highest age. Further, the point calculation unit 42 may set the maximum value of the leader points to be given as 1, and give the leader points having a value larger than 0 and equal to or smaller than 1 according to the age. Further, the point calculation unit 42 may include a rule such as ignoring the age difference and giving the same leader point when the age difference is equal to or smaller than a predetermined value from a person whose age rank is different by one.

  In addition, the point calculation unit 42 may use a rule such as giving a larger leader point as the position becomes higher. For example, leader points such as "10" are given to the president, "5" to the manager, and "2" to the section manager. In addition, the point calculation unit 42 may calculate the leader point according to the annual income and the amount of the pocket money of this month. For example, since a person with a higher annual income tends to have a higher influence, a value obtained by multiplying the annual income by a predetermined coefficient (eg, 0.1%) can be set to Ph [i]. In addition, the point calculation unit 42 may give a leader point such as “10” to the person with the highest annual income and “8” to the second person, and assign a predetermined leader point according to the order of the annual income. You may give. The same applies to this month's pocket money, but this month's pocket money may accurately represent the amount of money each person can use at restaurants, etc., so the influence may be more accurately estimated than using the annual income . Further, the point calculation unit 42 may calculate a leader point based on gender.

The second method is to add leader points calculated for each user attribute item by using a plurality of user attribute items. That is, the point calculation unit 42 calculates the leader point based on a plurality of items included in the attribute information of the user. First, the point calculation unit 42 calculates the leader point Ph [1] [i] related to the date of birth using the rule described above in the first method, “the leader point increases as the age increases”. calculate. Next, the point calculation unit 42 calculates the leader point Ph [2] [i] related to the position using the rule “the higher the position, the higher the leader point” described in the first method. In this way, the point calculation unit 42 calculates Ph [l] [i] (l = 1 to L, i = 1 to N) for the L user attribute items, and The leader point Ph [i] based on the user attribute information is calculated. Here, w [l] (l = 1 to L) is a predetermined weight coefficient determined for each user attribute item, and w [l]> 0.

  The third method is a method of calculating based on a rule set using a combination of a plurality of user items. For example, the point calculation unit 42 calculates the leader point of the user whose birth date is in a predetermined range and of a predetermined gender relatively high. For example, a woman of a specific age group tends to have a high demand level for a restaurant, so that the date of birth is within a predetermined range and the gender of a female user is given a predetermined leader point. This makes it easier for such user preferences to be reflected in group decision-making. This third method may be used alone or in combination with another method. For example, the point calculation unit 42 first calculates a leader point according to the equation (1), and then gives a leader point of “10” to a user whose birth date is within a predetermined range and of a predetermined gender. May be performed.

  A fourth method is to use a rule that takes into account the organization to which the user belongs. At that time, the point calculation unit 42 refers to the information stored in the user information storage unit 32. FIG. 22 shows a data structure of another database stored in the user information storage unit 32. This indicates a business relationship between the companies. This shows a situation in which company X orders a job from company Y, and company Y orders a job from company Z. Generally, the ordering company person often selects a restaurant or the like in accordance with the preference of the ordering company person, and such a tendency is reflected in the leader point. In the examples shown in FIG. 21 and FIG. 22, the leader point of the user of the workplace = X is the highest, the leader point of the user of the workplace = Y is the highest, and the leader point of the user of the workplace = Z is the lowest. . When there are a plurality of users who work at the same place, the leader point may be set to the same value, or a difference may be made using attribute items such as date of birth, gender, and post.

  The fifth method is a method that uses the relationship between the date of birth and the date (current date and time) at which processing is performed. For example, when the date on which the leader point is calculated is May 3, 2015, and the user whose date of birth is May 3, 1979 exists in the group, a birthday party for the person may be performed. Therefore, the point calculation unit 42 calculates the leader point of the person higher than usual. That is, the point calculation unit 42 calculates a relatively high leader point of the user whose processing date is on or near the birthday. As described above, the point calculation unit 42 calculates the leader point according to the relationship between the date on which the process is executed and the attribute information of the user.

  The sixth method is a method that uses the relationship between gender and the date (current date and time) at which processing is performed. For example, if the current date and time when the leader point is calculated is White Day on March 14, 2015, it is highly likely that a man will select a restaurant in accordance with the preference of a woman, so the point calculation section 42 determines the leader point of the woman. Is calculated higher than usual. In addition, the point calculation unit 42 may use any one of the first to sixth methods described above alone, or may use a combination of a plurality of methods.

Next, the point calculation unit 42 calculates the overall leader point P [i] as follows.
Here, the leader point based on the position information of the terminal i described in the first embodiment is denoted by Pg [i], and the leader point based on the user attribute information is denoted by Ph [i]. Α1 and α2 are predetermined coefficients that satisfy α1> 0 and α2> 0. That is, the point calculation unit 42 performs weighted addition of the leader point based on the position information and the leader point based on the user attribute information. Note that assuming α1 = 1 and α2 = 1, the sum (addition value) of the leader point based on the position information and the leader point based on the user attribute information may be used as the overall leader point.

The point calculation unit 42 may calculate the total leader point P [i] as follows.
Here, γ1 and γ2 are predetermined numbers that satisfy γ1> 0 and γ2> 0. That is, a product (multiplied value) of a power value having Pg [i] as a radix and γ1 as an exponent and a power value having Ph [i] as a radix and γ2 as an exponent is P [i]. When it is desired to strongly reflect Pg [i] on Ph [i], γ1 is set to a large value. When it is desired to strongly reflect Ph [i] on Ph [i], γ2 is set to a large value. Note that, with γ1 = 1 and γ2 = 1, the product (multiplied value) of the leader point based on the position information and the leader point based on the user attribute information may be used as the overall leader point. After the point calculation unit 42 calculates the total leader point P [i] based on the equation (2) or the equation (3), the same processing as in the first embodiment may be executed.

  As in the first embodiment, the information providing apparatus 10 creates display data and transmits it to the management client 18. The management client 18 can display graphs and tables as described below. FIG. 23 shows information displayed on the management client 18 according to the second embodiment of the present invention, which is a result of classifying visitors of a store by gender and age, and further counting the number of people by leader point category. Is an example of displaying in a table format. The aggregation processing unit 26 combines the user attribute table shown in FIG. 21 and the point history table 44 shown in FIG. 11 using the terminal ID as a key, and aggregates the items, thereby obtaining data for displaying this table. Can be

  FIG. 24 shows another information displayed on the management client 18 according to the second embodiment of the present invention, which is an example in which the data of FIG. 23 is displayed in a graph. Displaying the graph makes it easier to intuitively grasp the characteristics of the store. From this graph, it can be seen that the percentage of buyers with high leader points is high for women in their teens and men in their thirties. Customers with high leader points are more likely to attract new customers, so it is considered that the promotion effect targeting women in their teens and men in their thirties is high. On the other hand, those with low leader points are more likely to accept the opinions and suggestions of others and follow them. In particular, it is expected to be relatively susceptible to celebrity remarks. In the example shown in this figure, the percentage of purchasers with low leader points is high among women in their 30s. Therefore, appointing a celebrity popular with women in their thirties for advertisement or transmitting information on this store can be determined to be effective measures to increase sales at this store.

  FIG. 25 shows still another information displayed on the management client 18 according to the second embodiment of the present invention, which divides the visitors of a nationwide famous store located in the Kanto area by living area. This is an example in which the number of persons is classified, the number of persons is further counted for each leader point category, and displayed in a table format. Such data can also be obtained by the tallying processing unit 26 combining the user attribute table and the point history table 44 with the terminal ID as a key, and tallying each item.

  FIG. 26 shows still another information displayed on the management client 18 according to the second embodiment of the present invention, which is an example in which the same data as FIG. 25 is displayed in a graph. In shopping malls and other facilities, it is important to attract not only local residents but also visitors from far away, but it will be costly to expand advertising nationwide. Therefore, although the number of visitors from a distance is relatively small compared to the neighboring areas, for example, since the number of visitors from Shikoku has a high leader point, promotion targeting Shikoku is performed This is the most efficient way to increase the number of visitors from far away.

  According to the present embodiment, the leader point is calculated according to the attribute information of the user corresponding to the subject, so that the estimation accuracy of the influence of the users in the group can be improved. Further, since the leader point is calculated based on a plurality of items included in the user attribute information, the estimation accuracy can be improved. In addition, since the leader point is calculated according to the relationship between the date on which the process is executed and the attribute information of the user, the influence can be estimated while considering the date on which the process is executed. In addition, in addition to the location information of the terminal device, the leader point can be calculated with higher accuracy using the attribute information of the user who uses the terminal device, so that effective information can be obtained by the store operator or the shopping mall operating organization. Can be provided. In addition, by using the attribute information of the user, the influence of the members in the group regarding the store selection and the product selection can be quantified accurately as a leader point. In addition, since it is possible to perform tallying by freely combining the attribute information of the user and the leader point, it is possible to analyze the tendency of a visitor or a purchaser from an unconventional viewpoint. For this reason, the promotion method and the accuracy of target selection can be improved.

(Example 3)
Next, a third embodiment will be described. Example 3 relates to an information providing system as before. Similarly to the second embodiment, the information providing apparatus 10 according to the third embodiment also forms a group using the position information of the plurality of terminal devices 16 and estimates the influence of each user in the group. In the third embodiment, it is assumed that the group is a family. The information providing system 100 and the information providing apparatus 10 according to the third embodiment are of the same type as those in FIGS. Here, the difference will be mainly described.

  First, when forming a group in step S100, the accuracy of group formation is improved by using user attribute information. Further, the group forming unit 40 changes the condition for forming the group according to the attribute information of the user corresponding to the subject. The user attribute information is stored in the user information storage unit 32.

  FIG. 27 illustrates a data structure of another database stored in the user information storage unit 32 according to the third embodiment of the present invention. As shown, the terminal ID is registered for each family. Referring back to FIG. When the terminal IDs are registered for each family, the group forming unit 40 relaxes the condition for determining these terminal devices 16 as one group as compared with a normal case. For example, when determining the terminal devices 16 existing within a predetermined distance for a predetermined time or more as one group, the group forming unit 40 sets the threshold value of the distance between the terminals longer than usual, or sets the predetermined time to a predetermined value. Or shorten it. The process is based on the assumption that families are likely to act together toward a common destination. For example, in the process of heading for the destination, even if the child temporarily goes away first, it is possible to maintain the determination as a group action and provide appropriate information.

  Next, in step S110, the point calculation unit 42 uses the date of birth and the relationship registered in the database shown in FIG. 27 to determine the leader point based on the positional information of the child of a predetermined age or less (for example, elementary school student or less). Perform processing to make it lower than the parent. For example, even if the number of times that a child is located at the head of the group is large, the point calculation unit 42 performs processing that does not reflect much on the leader point based on the location information. Specifically, the point calculating unit 42 multiplies the number of times the head position is observed by 0.1 for the terminal device 16 of a child under a predetermined age, or sets an upper limit on the number of head positions used for leader point calculation. Or other control processing. Small children often act impulsively or suddenly, but by performing such control processing, it is possible to accurately calculate a leader point based on position information.

  In addition, since the mother generally has the strongest influence in the home, the point calculation unit 42 may perform processing to maximize the mother's leader point. Further, the point calculation unit 42 may calculate using the relevance between the month and day (current date and time) at which the processing is performed and the domestic relationship. For example, when the current date and time for calculating the leader point is June 21, 2015, which corresponds to Father's Day, the point calculation unit 42 may calculate the father's leader point higher than usual.

  Alternatively, information regarding the type (role) of a member in a group may be stored in the user attribute information, and the point calculation unit 42 may calculate a leader point using the information. For example, since a tour guide in a group trip is frequently located at the head of a group, a member type such as “guide” or “customer” is stored in the user attribute information, and the number of leader points of the “guide” is reduced, or Processing such as exclusion from the leader point calculation target may be performed.

  According to the present embodiment, conditions for forming a group are changed in accordance with the attribute information of the user corresponding to the subject, so that a group suitable for circumstances such as a family can be formed. Further, since the leader point is calculated according to the family structure, the influence within the family can be estimated with high accuracy.

(Example 4)
Next, a fourth embodiment will be described. Example 4 relates to an information providing system as before. In the first embodiment, statistical values such as a distribution (frequency) of the leader points for each item by category, a representative value of the leader points for each item, and a value indicating the degree of variation are calculated. On the other hand, in the fourth embodiment, the statistical value of the leader point for each terminal device 16 (or each user) is calculated, and the item point for each item is calculated based on the statistical value. The information providing system 100 and the information providing apparatus 10 according to the fourth embodiment are of the same type as those in FIGS. Here, the difference will be mainly described.

  The tallying processing unit 26 in FIG. 20 scans the point history table 44 at a predetermined timing, and calculates the following index for each terminal ID, that is, an index (statistical value) in which leader points are aggregated. The predetermined timing may be a predetermined time interval such as once a day, once an hour, or the like, or may be a timing at which new data is registered in the point history table 44. Further, the processing may be performed on all data stored in the point history table 44, or may be performed only on data corresponding to a predetermined period (such as the latest one month).

(1) Representative Value of Leader Point The tallying processing unit 26 calculates a representative value of a leader point for each terminal device 16. Any of an average value, a median value, and a mode value can be used as the representative value, but in the following description, the average value is used. For example, three leader points of a certain terminal device 16 (the Xth terminal device 16x) are recorded in the point history table 44, and are respectively "0.6", "0.4", and "0.2". In this case, the average value is “(0.6 + 0.4 + 0.2) ÷ 3 = 0.4”. Alternatively, the average value may be calculated after correcting the leader points according to the number of people in the group. In a case where the sum of the leader points is normalized to be “1” in one group, the average value (expected value) of the leader points per person decreases as the number of groups increases. Further, even when the number of members is not normalized, when the number of members in the group increases, the frequency at which each member is located at the head of the group decreases, so that the leader point per person tends to decrease.

  Therefore, when the number of groups is large, a correction process such as increasing the value of the leader point is performed. For example, when the number of persons in a certain group is 5 or more and less than 8 persons, the leader point is increased by 1.25 times, and when the number is 8 or more and less than 12 persons, the leader point is increased by 1.5 times. If the number is 12 or more, the leader point is multiplied by "2.0". Assuming that the number of groups when the leader point of the X-th terminal device 16x is calculated is “3”, “6”, and “12”, respectively, “(0.6 + 0.4 × 1.25 + 0 .2 × 2.0) ÷ 3 = 0.5 ”is the average value (average value with correction).

(2) Value indicating the degree of variation in leader points The aggregation processing unit 26 calculates a value indicating the degree of variation in leader points of the Xth terminal apparatus 16x. For example, standard deviation, variance, (maximum value-minimum value), quartile range (third quartile-first quartile) can be used, but in the following description, standard deviation is used. Shall be.

(3) Frequency (frequency) by leader point category
As in step S310, the tallying processing unit 26 associates the leader point with the leader point category using a predetermined rule, and calculates the number of data (frequency, frequency) for each terminal device 16 (user) for each category. . Note that, as described in step S310, the classification criteria may be changed according to the number of people in the group.

(4) Relative frequency for each leader point category The aggregation processing unit 26 relativizes the frequency for each leader point category calculated in (3). Specifically, for each terminal device 16 (user), a relative frequency is calculated such that the total of the frequencies becomes “1”. For example, when the frequency of each category of the Xth terminal apparatus 16x is “Rp large = 8”, “Rp medium = 12”, and “Rp small = 5”, each relative frequency is “Rp large = 8 ÷”. (8 + 12 + 5) = 0.32 "," Rp = 12 ÷ (8 + 12 + 5) = 0.48 ", and" Rp small = 5 ÷ (8 + 12 + 5) = 0.2 ".

  Note that the tallying processing unit 26 may calculate the above-described indices (1) to (4) for each combination of the terminal device 16 and the store genre. Such calculation can be performed by connecting the store information table and the point history table 44 using the store ID as a key. For example, for a certain terminal device 16 (the Xth terminal device 16x), “Rp representative value of (Xth terminal device 16x, Japanese food) = 0.2”, “Rp representative value of (Xth terminal device 16x, Western food) = An index such as “0.5”, “Rp representative value of (X-th terminal device 16x, Chinese) = 0.3” may be calculated. The same applies to the index indicating the degree of variation of the leader point of (2).

  In addition, the aggregation processing unit 26 may calculate, as the relative frequency for each category (4), a relative frequency such that the sum of the frequencies is “1” for each combination of the terminal device 16 and the store genre. For example, “Rp large relative frequency of (Xth terminal device 16x, Japanese food) = 0.1”, “Relative frequency of Rp of (Xth terminal device 16x, Japanese food) = 0.4”, “(Xth Rp small relative frequency of terminal device 16x, Japanese food = 0.5 "," Rp large relative frequency of (Xth terminal device 16x, western food) = 0.4 "," (Xth terminal device 16x, western food) Rx of Rp = 0.4 ”,“ Rp small relative frequency of (Xth terminal device 16x, Western food) = 0.2 ”, etc. may be calculated.

  The aggregation processing unit 26 calculates each index by the above-described method, and stores the result in the user-specific point aggregation table 46 inside the aggregation processing unit 26. FIG. 28 shows the data structure of the user-specific point totaling table 46 according to the fourth embodiment of the present invention. As shown in the figure, the terminal ID (user ID) is associated with each of the indices (1) to (4) and stored. FIG. 29 shows the data structure of another user-specific point totaling table 46 according to the fourth embodiment of the present invention. This figure is an example in which respective indices are calculated and stored for each combination of the terminal device 16 and the store genre.

  Further, the tallying processing unit 26 creates and stores a purchase history table 48 as shown in FIG. 30 based on the POS data of each store. FIG. 30 shows a data structure of the purchase history table 48 according to the fourth embodiment of the present invention. As shown in the figure, a store ID, a terminal ID (user ID), a product ID, a quantity, and a purchase date and time are stored in association with each other. The terminal ID (user ID) may be acquired from the terminal device 16 using short-range wireless communication at a cash register in a store, or may be acquired in advance in cooperation with a customer's point card. The store operator can use the purchase history table 48 to know who, when, where, what, and how many purchases have been made.

  In this manner, the tallying processing unit 26 calculates the item points for each item by tallying the statistical value of the leader point for each terminal ID calculated by the point calculation unit 42, for example, the average value. Further, the tally processing unit 26 creates display data to be displayed on the management client 18 based on the user-specific point tally table 46 and the purchase history table 48. Then, the control unit 30 transmits the display data to the management client 18 via the transmission / reception unit 20.

  When receiving the display data from the information providing device 10, the management client 18 displays information related to the leader point on the display unit based on the display data. FIG. 31 shows information displayed on the management client 18 according to the fourth embodiment of the present invention, which is an example of display of leader point information. It is a table | surface which shows the average value (Rp average value) of the leader point for every shop, and the standard deviation (Rp standard deviation) of a leader point. This data can be created by connecting the user-specific point totalization table 46 and the purchase history table 48 using the terminal ID as a key. For example, in the example of the purchase history table 48 shown in FIG. 30, the store “S3” is used by two terminal IDs “A” and “B”, and the terminal ID “A” is used as in the example shown in FIG. , And “B” are respectively “0.2” and “0.5”, the average value “(0.2 + 0.5) ÷ 2 = 0.35” is converted to the store “S3 Can be calculated as item points. That is, the item points for each item can be calculated based on the average value of the leader points for each terminal device 16. When one terminal device 16 uses a store multiple times, the average value may be calculated in consideration of the number of times of use, or may not be considered. For example, the weighted average may be calculated so that the terminal device 16 that uses the terminal more frequently has a larger weighting coefficient. Alternatively, an average value may be calculated by setting the weighting factors of all terminal devices 16 to “1” regardless of the number of times of use. Further, the average value may be calculated in consideration of the purchased product quantity. Next, the standard deviation of the leader point is calculated for each store using the average value calculated in this way. For example, in the above example, when the standard deviation of the store “S3” is calculated, “SQRT (((0.2−0.35) ＾ 2 + (0.5−0.35) ＾ 2) ÷ 2) = 0 .15 ". Here, "SQRT" represents a square root, and "$ 2" represents a square. Further, when the process is simply performed, a process of calculating the average value of the standard deviation of each terminal device 16 and regarding the average value as an approximate value of the standard deviation of the leader point may be performed.

  It can be seen that the average value of the leader points of the customers who visit the store (customers who purchased the products) is higher in the store S21 than in the store S22. Further, when comparing the store S23 and the store S24, it can be seen that the Rp average value is the same, but the Rp standard deviation is smaller in S23. From this data, it can be seen that the customer in S24 has less variation in leader points than the customer in S23. That is, it can be said that the customer of S24 is stable with the leader point not changing much from time to time as compared with the customer of S23. FIG. 32 shows another information displayed on the management client 18 according to the fourth embodiment of the present invention, which is an example in which the data shown in FIG. 31 is displayed in a graph format. With such a graph display, the store operator can more intuitively grasp the difference in characteristics between stores.

  FIG. 33 shows still another information displayed on the management client 18 according to the fourth embodiment of the present invention, which is an example in which the distribution of the leader point category for each product is displayed in a table format. FIG. 34 shows further information displayed on the management client 18 according to the fourth embodiment of the present invention, and is an example in which the same data as FIG. 33 is displayed in a graph. This data is obtained by combining the user-specific point totalization table 46 and the purchase history table 48 using the terminal ID as a key, and totalizing the category-specific frequency or category-specific relative frequency of the user-specific point totalization table 46 for each product ID. Obtained. Specifically, the frequencies (or relative frequencies) are summed up for each of the categories of “Rp large”, “Rp medium”, and “Rp small”, and further, the relativization is performed so that the sum of the frequencies for each product becomes “1”. I do. That is, the item points are calculated by further summing up the leader points for each combination of the terminal ID (user ID) and the predetermined category recorded in the user-based point totaling table 46 for each item. it can.

  For example, there are three customers C, D, and E who have purchased the product I2, and the frequency of each category is “C large Rp = 10”, “D large Rp = 20”, and E large Rp = 5. "," C during Rp = 20 "," D during Rp = 10 "," E during Rp = 20 "," C low Rp = 5 "," D low Rp = 0 "," E low Rp small = 10 ". In this case, “sum of Rp = 10 + 20 + 5 = 35”, “sum of Rp = 20 + 10 + 20 = 50”, and “sum of Rp small = 5 + 0 + 10 = 15”. Then, when each is made relative, “relative value of large Rp = 35 ＝ 100 = 0.35”, “relative value in Rp = 50 ÷ 100 = 0.5”, “relative value of small Rp = 15 ÷ 100” = 0.15 ". Similar processing may be performed for other products. When the purchase history table 48 is totaled, the total may be totaled for each store or may be totaled over a plurality of stores. That is, the total may be calculated for each combination of the product ID and the store ID, or may be calculated for each product ID regardless of the store ID. Alternatively, as another method, the number of terminal devices 16 (or the number of users) may be counted and set as the frequency for each category. Specifically, for each terminal device 16, one category having the highest frequency is specified, and the frequency of that category is set to "1", and the other frequencies are set to "0", and the frequencies for each category are totaled for each item. do it. For example, in the above numerical example, the customer C has the highest frequency of “during Rp”, so “during Rp of C = 1”, and the other categories are “0”. For the customer D, “Rp of D = 1”, and the other categories are “0”. For the customer E, “Rp of E = 1”, and the other categories are “0”. As a result, the category-specific frequencies of the leader point of the product I2 are “Rp large = 1”, “Rp medium = 2”, and “Rp small = 0”.

  By browsing FIG. 33, the store operator finds that “product I1 has a high Rp ratio, so demand is likely to increase in the future”, “product I2 and product I4 have similar distribution tendencies, and therefore are the same. It is possible that sales will increase if the two product shelves are placed close to each other. "

  FIG. 35 shows still another information displayed on the management client 18 according to the fourth embodiment of the present invention, which shows, in a table format, a store genre and an Rp average value for each store genre for each store. This is an example. The store genre of the store S21 is “Japanese food”, and the average Rp value of Japanese food is higher than that of Western food and Chinese food. For this reason, it can be said that many visitors who visit S21 exert a strong influence when selecting a Japanese restaurant. The operator of S21 can make a judgment such as "pursuing authentic Japanese food without incorporating other cooking genres into the menu will lead to a thriving restaurant." The store S22 also has the store genre “Japanese food”, but the Western food Rp average value is higher than the Japanese food Rp average value. For this reason, it can be said that many visitors who visit S22 exert a strong influence when selecting a Western restaurant. By performing such display, the operator of S22 can formulate a strategy such as "If a joint campaign with a nearby Western restaurant is carried out, the number of customers may increase". In store S24, the store genre is “Chinese”, but the Rp average value of Japanese food is considerably high. For this reason, the operator of S24 can make predictions such as “there is a high possibility that sales will increase if Japanese food is included in the menu”.

  In addition, this embodiment can be easily applied not only to an actual shop but also to an Internet shop (net shop) and the like. That is, the user-specific point totaling table 46 is created based on the user behavior for the actual store, but the purchase history table 48 is not limited to the actual store, and may be that of an online store. In addition, an actual store and an online store may be mixed. As described above, the information (item points) indicating the characteristics of the online store and the characteristics of the products handled in the online store can be created using the leader points calculated based on the user behavior relating to the actual store. Can be applied to

  According to the present embodiment, the characteristics of the customer can be reflected in the provided information, not only when the customer visits the store as a group but also when the customer visits the store alone. Further, since the characteristics of the customer can be easily presented not only for each store but also for each product, the present invention can be applied to a product purchase plan and the like.

(Example 5)
Next, a fifth embodiment will be described. The fifth embodiment relates to the information providing system as before. Until now, the leader point has been calculated based on the characteristic behavior of the user in one group behavior. On the other hand, in the fifth embodiment, the leader point calculated in the past group behavior is reflected on the leader point in the current group behavior. Specifically, the past leader points of other users are reflected on the leader points of a certain user (one user) in the group. The information providing system 100 and the information providing apparatus 10 according to the fifth embodiment are of the same type as that of FIG. 1, FIG. 2, or FIG. Here, the difference will be mainly described.

  In the present embodiment, after executing step S110, the information providing apparatus 10 executes step S115 (not shown) before executing step S120. FIG. 36 illustrates a data structure of a database stored in the group information processing unit 24 according to the fifth embodiment of the present invention. In the following description, the group ID of the processing target group (current group) is “G21”, and in the group information table, “G21” has three “C”, “D”, and “E”. It is assumed that the terminal device 16 is compatible. The three terminal devices 16 correspond to a third terminal device 16c, a fourth terminal device 16d, and a fifth terminal device 16e. Further, the leader points (temporary points) at a certain point in time are “C = 80”, “D = 100”, and “E = 60”.

  It is assumed that the data shown in FIG. 37 is stored in the point history table 44. FIG. 37 shows a data structure of the point history table 44 according to the fifth embodiment of the present invention. As described above, the point history table 44 is a table in which past leader points for each terminal ID are shown, for example, a table in which calculation dates and times and leader points are associated with each other.

  FIG. 38 is a flowchart showing a procedure for correcting a leader point by the information providing apparatus 10 according to the fifth embodiment of the present invention, and is a flowchart showing details of step S115. First, in step S400, the point calculation unit 42 ranks the terminal devices 16 in the target processing group in descending order of the leader points (temporary points). In the example described above, “D = 1st”, “C = 2nd”, and “E = 3rd”.

  Next, in step S410, the point calculation unit 42 acquires a past leader point for the terminal device 16 in the predetermined order. Here, the predetermined order is each order from the second place to a certain place (X place) below the second place. More specifically, the point calculation unit 42 first obtains a past leader point with reference to the point history table 44 stored in the tally processing unit 26 for the second-ranked terminal device 16. In the above example, the past leader point of the third terminal device 16c is obtained. In the example of FIG. 37, the point calculation unit 42 determines three points of “(G11, C) = 0.4”, “(G12, C) = 0.6”, and “(G13, C) = 0.8”. Get the data. X may be the second place, and the predetermined order may be only the second place. If X = third and the predetermined ranking is “2nd and 3rd”, the point calculation unit 42 further acquires the past leader points of the fifth terminal device 16e.

  In the example of FIG. 37, there are two of “(G11, E) = 0.05” and “(G12, E) = 0.15”. In addition, the point calculation unit 42 may acquire the past leader points limited to the group ID that does not include the terminal device 16 at the top. For example, when acquiring the past leader points for the second terminal device 16, the point calculation unit 42 acquires the terminal ID excluding the group ID including the fourth terminal device 16 d. In the above example, since the fourth terminal device 16d is included in G11, the point calculation unit 42 excludes G11, and sets “(G12, C) = 0.6” and “(G13, C) = 0.8. May be obtained. The point calculation unit 42 also obtains the past leader points of the third terminal device 16 that do not include the first terminal device 16. As described above, by acquiring past leader points limited to data that does not include the terminal device 16 at the top, the correction accuracy of leader points, which will be described later, may be improved.

  Next, in step S420, the point calculation unit 42 calculates a representative value of a past leader point for each terminal device 16 using the acquired leader point. Any of an average value, a median value, and a mode value can be used as the representative value, but in the following description, the average value is used. In the example described above, the average value of the third terminal device 16c is “(0.4 + 0.6 + 0.8) ÷ 3 = 0.6”. The average value of the fifth terminal device 16e is “(0.05 + 0.15) ÷ 2 = 0.1”.

  Next, in step S430, the point calculation unit 42 determines whether the calculated representative value satisfies a predetermined condition, and corrects the first leader point according to the determination result. Specifically, the point calculation unit 42 sets the representative value to be equal to or greater than the predetermined value θ1 as a predetermined condition, and increases the number of first leader points when the condition is satisfied. For example, when “θ1 = 0.3”, in the above-described example, the third terminal device 16c satisfies this condition, and the first-place leader point “D = 100” is multiplied by 1.5 to “D = 100”. 150 "or" 30 "is added to make" D = 130 ". That is, the point calculation unit 42 corrects the leader point by multiplying the first leader point by the predetermined coefficient or adding a predetermined number. In the case of the terminal devices 16 (users) in the second and lower ranks, if there are terminal devices 16 (users) with a higher leader point in the past, the influence of the terminal device 16 (user) with the higher leader point is higher than those. Can be estimated to be very strong. In other words, a person having a higher leader point than a person having a high leader point in the past can be estimated as a person having a very strong influence. For this reason, the point calculation unit 42 performs a process of setting the leader point higher than usual.

  In addition, the point calculation unit 42 may set a predetermined condition that the representative value is less than the predetermined value θ2, and may perform a process of reducing the number of first leader points when the condition is satisfied. For example, when “θ2 = 0.2”, in the above example, the fifth terminal device 16e satisfies this condition, and the first leader point “D = 100” is multiplied by 0.8 to obtain “D = 80, or "10" is subtracted to obtain "D = 90". In the case of the terminal device 16 (user) of the second or lower rank, if there is a terminal device 16 (user) having a low past leader point, the influence of the terminal device 16 (user) of the first rank in the current group is determined by other users. Is estimated to be stronger than the entity because of the weak influence. Therefore, the point calculation unit 42 performs a process of lowering the leader point than usual.

  Further, the point calculation unit 42 may change the degree of increasing or decreasing the leader points according to the number of the terminal devices 16 whose representative values are equal to or larger than the predetermined value θ1. For example, when the number of the terminal devices 16 whose representative values are equal to or more than θ1 is one, the point calculation unit 42 “1.5 times”. When the number of the terminal devices 16 is two, “two times”. In such a case, a process such as "3 times" may be performed. In addition, the point calculation unit 42 sets “double” when the representative value of the second-rank terminal device 16 is equal to or more than θ1, and sets “1.5” when the representative value of the third-rank terminal device 16 is equal to or more than θ1. “2 × 1.5 = 3 times” when both the second and third place terminal devices 16 are equal to or larger than θ1. That is, the point calculation unit 42 may change the degree of the leader point correction according to the order of the terminal devices 16 whose representative values satisfy the predetermined condition.

  In the above description, the leader point of the terminal device 16 of the first place is corrected. However, when the number of the terminal devices 16 included in the group is large, the leader point of the terminal device 16 of the second place or less is similarly considered. The points may be corrected. For example, the leader points of the second terminal device 16 may be increased or decreased using the past leader points of the third terminal device 16. That is, the point calculation unit 42 calculates a leader point for one subject included in the group, based on a previously calculated leader point corresponding to another subject included in the group.

  According to this embodiment, for one subject included in the group, the leader point is calculated based on the previously calculated leader points corresponding to the other subjects included in the group. Can be improved. In addition, since the leader points of the processing target group are corrected using the past leader points accumulated in the point history table, the influence of the user can be more accurately reflected on the leader points.

  The present invention has been described based on the embodiments. This embodiment is an exemplification, and it is understood by those skilled in the art that various modifications can be made to the combination of each component and each processing process, and that such modifications are also within the scope of the present invention. .

Embodiments 1 to 5 may be defined by the following items.
[Item 1]
A group forming unit that acquires position information of a subject that is a user or a terminal device, and forms a group that includes a plurality of subjects based on time-series data of the position information related to the plurality of subjects.
Based on the time-series data of the position information, for each of a plurality of subjects included in the group formed by the group forming unit, a predetermined behavior pattern that is considered to have a high influence of the subject in the group is matched. A point calculation unit that calculates a characteristic action amount indicating a degree of execution, and calculates a point for each subject based on the characteristic action amount;
A totaling processing unit that calculates an item point for each item by totalizing the points calculated in the point calculating unit;
An output unit that outputs the item points calculated in the aggregation processing unit,
An information providing device comprising:

[Item 2]
A group forming unit that acquires position information of a subject that is a user or a terminal device, and forms a group that includes a plurality of subjects based on time-series data of the position information related to the plurality of subjects.
Based on the time-series data of the position information, for each of the plurality of subjects included in the group formed by the group forming unit, a predetermined behavior pattern is determined that the influence of the subject in the group is high. A point calculation unit that calculates a characteristic action amount indicating a degree of execution, and calculates a point for each subject based on the characteristic action amount;
An output unit that outputs the points calculated by the point calculation unit in association with information on items,
An information providing device comprising:

[Item 3]
3. The item counting method according to item 1, wherein the tallying unit calculates item points for each item by counting points for each item by category using a predetermined category corresponding to the size of the points. Information providing device.

[Item 4]
2. The information providing apparatus according to item 1, wherein the tallying unit calculates an item point for each item using a statistical value of points calculated by the point calculating unit.

[Item 5]
The aggregation processing unit uses a statistical value obtained by aggregating the points calculated by the point calculation unit for each subject, or a value obtained by aggregating the points calculated by the point calculation unit for each combination of the subject and a predetermined category, The information providing device according to item 1, wherein an item point for each item is calculated.

[Item 6]
The point calculation unit determines at least one of the number of times, the matching time, the matching moving distance, and the matching probability that the movement pattern of the subject in the time-series data of the position information matches the predetermined action pattern. The information providing apparatus according to any one of items 1 to 5, wherein the information is calculated as the characteristic action amount, and the point is calculated such that the point becomes higher as the characteristic action amount increases.

[Item 7]
The one of items 1 to 6, wherein the predetermined action pattern in the point calculation unit is a pattern in which the subject is located at a position of a predetermined order from the head of the group with reference to the moving direction of the group. Information providing device as described.

[Item 8]
8. The information providing apparatus according to item 7, wherein the point calculation unit calculates points that are higher as the predetermined order is higher.

[Item 9]
The predetermined behavior pattern in the point calculation unit is a pattern in which, when a group is divided into a plurality of groups and then merges, the main body moves from the time of division in a direction in which the group moves after merging. 9. The information providing device according to any one of items 1 to 8.

[Item 10]
The information providing apparatus according to item 9, wherein the point calculation unit calculates points based on the number of subjects included in the small group when the group is divided into a plurality of groups.

[Item 11]
The information according to any one of items 1 to 10, wherein the predetermined action pattern in the point calculation unit is a pattern in which, when the subject stops, the other subjects also stop following the stop. Providing device.

[Item 12]
12. The information providing apparatus according to item 11, wherein the point calculation unit calculates points based on the number of other subjects stopped following the stop.

[Item 13]
13. The information providing apparatus according to any one of items 1 to 12, wherein the point calculation unit calculates points according to user attribute information corresponding to the subject.

[Item 14]
14. The information providing apparatus according to any one of items 1 to 13, wherein the point calculation unit calculates points according to a distance between the position of the item and the position of the subject.

[Item 15]
The information providing apparatus according to any one of items 1 to 14, wherein the group forming unit changes a condition for forming a group according to attribute information of a user corresponding to the subject.

[Item 16]
A management client communicable with the information providing device according to item 1,
From the information providing device, a receiving unit that receives the item point,
A control unit that performs control to display the received data on a display unit,
A management client, comprising:

[Item 17]
A management client communicable with the information providing apparatus according to item 2,
From the information providing device, a receiving unit that receives the information on the points and items,
A control unit that performs control to display the received data on a display unit,
A management client, comprising:

[Item 18]
Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Calculating the item points for each item by aggregating the calculated points;
Outputting the calculated item points;
An information providing method, comprising:

[Item 19]
Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Outputting the calculated points in association with the information on the item;
An information providing method, comprising:

[Item 20]
Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Calculating the item points for each item by aggregating the calculated points;
Outputting the calculated item points to the computer.

[Item 21]
Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Outputting the calculated points in association with the information related to the item.

  DESCRIPTION OF SYMBOLS 10 information provision apparatus, 12 network, 14 base station apparatuses, 16 terminal apparatuses, 18 management client, 20 transmission / reception part, 22 position information management part, 24 group information processing part, 26 tally processing part, 28 store information storage part, 30 Control section, 32 user information storage section, 40 group formation section, 42 point calculation section, 44 point history table, 46 point totalization table by user, 48 purchase history table, 100 information providing system.

Claims (16)

A group forming unit that acquires position information of a subject that is a user or a terminal device, and forms a group that includes a plurality of subjects based on time-series data of the position information related to the plurality of subjects.
Based on the time-series data of the position information, for each of the plurality of subjects included in the group formed by the group forming unit, a predetermined behavior pattern is determined that the influence of the subject in the group is high. A point calculation unit that calculates a characteristic action amount indicating a degree of execution, and calculates a point for each subject based on the characteristic action amount;
A totaling processing unit that calculates an item point for each item by totalizing the points calculated in the point calculating unit;
An output unit that outputs the item points calculated in the aggregation processing unit,
An information providing device comprising: A group forming unit that acquires position information of a subject that is a user or a terminal device, and forms a group that includes a plurality of subjects based on time-series data of the position information related to the plurality of subjects.
Based on the time-series data of the position information, for each of the plurality of subjects included in the group formed by the group forming unit, a predetermined behavior pattern is determined that the influence of the subject in the group is high. A point calculation unit that calculates a characteristic action amount indicating a degree of execution, and calculates a point for each subject based on the characteristic action amount;
An output unit that outputs the points calculated by the point calculation unit in association with information on items,
An information providing device comprising:   2. The method according to claim 1, wherein the aggregation processing unit calculates an item point for each item by using a predetermined category corresponding to the size of the point, and aggregating points for each item for each category. 3. Information providing device.   The information providing device according to claim 1, wherein the aggregation processing unit calculates an item point for each item using a statistical value of the points calculated by the point calculation unit.   The aggregation processing unit uses a statistical value obtained by aggregating the points calculated by the point calculation unit for each subject, or a value obtained by aggregating the points calculated by the point calculation unit for each combination of the subject and a predetermined category, The information providing apparatus according to claim 1, wherein an item point for each item is calculated.   The point calculation unit determines at least one of the number of times, the matching time, the matching moving distance, and the matching probability that the movement pattern of the subject in the time-series data of the position information matches the predetermined action pattern. The information providing apparatus according to claim 1, wherein the point is calculated as the characteristic action amount, and the point is calculated such that the point becomes higher as the characteristic action amount increases.   The said predetermined action pattern in the said point calculation part is a pattern in which a main body is located in the position of a predetermined order from the head of a group with reference to the moving direction of a group, The any one of Claim 1 to 6 characterized by the above-mentioned. Information providing device according to the item.   The said predetermined action pattern in the said point calculation part is a pattern in which the main body has moved from the time of division in the direction which moves after merging, when a group divides into several and merges. 8. The information providing device according to any one of 1 to 7.   The said predetermined action pattern in the said point calculation part is a pattern which, when a main body stops, follows the said stop, and also the other main bodies stop, The one of Claim 1 to 8 characterized by the above-mentioned. Information providing device.   The information providing apparatus according to claim 1, wherein the point calculation unit calculates the points also according to user attribute information corresponding to the subject.   The information providing apparatus according to claim 1, wherein the point calculation unit calculates a point according to a distance between a position of the item and a position of the subject.   The information providing apparatus according to claim 1, wherein the group forming unit changes a condition for forming a group in accordance with attribute information of a user corresponding to the subject. An information providing method executed by a computer,
Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Calculating the item points for each item by aggregating the calculated points;
Outputting the calculated item points;
Information providing method characterized in that it comprises a. An information providing method executed by a computer,
Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Outputting the calculated points in association with the information on the item;
Information providing method characterized in that it comprises a. Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Calculating the item points for each item by aggregating the calculated points;
Outputting the calculated item points to the computer. Acquiring position information of a subject that is a user or a terminal device, and forming a group including a plurality of subjects based on time-series data of the position information related to the plurality of subjects,
Based on the time-series data of the position information, a characteristic indicating a degree of matching with a predetermined action pattern in which the influence of the subject in the group is high for each of the plurality of subjects included in the formed group. Calculating an activity amount, and calculating points for each subject based on the characteristic activity amount;
Outputting the calculated points in association with the information related to the item.